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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - text-to-cad
+  - code-generation
+  - cadquery
+  - 3d-modeling
+  - reinforcement-learning
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# CAD-Coder
+
+**CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward**
+
+This is the reinforcement learning (GRPO) fine-tuned model for generating CadQuery code from natural language descriptions.
+
+## Model Description
+
+CAD-Coder reformulates text-to-CAD as the generation of CadQuery scripts—a Python-based, parametric CAD language. The model is trained with a two-stage pipeline:
+
+1. **Supervised Fine-Tuning (SFT)**: Learning CadQuery syntax and text-to-code mapping
+2. **Reinforcement Learning (GRPO)**: Optimizing geometric accuracy with CAD-specific rewards (Chamfer Distance + Format Reward)
+
+### Key Features
+
+- Generates executable CadQuery Python code from natural language
+- Chain-of-Thought (CoT) reasoning for complex CAD structures
+- Geometric reward optimization for accurate 3D model generation
+- Supports diverse CAD operations beyond simple sketch-extrusion
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "gudo7208/CAD-Coder"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+
+# Example prompt
+prompt = """Create a cylinder with radius 10mm and height 20mm, with a central hole of radius 5mm."""
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.8, top_k=20)
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+## Performance
+
+| Method | Mean CD | Median CD | IR% |
+|--------|---------|-----------|-----|
+| Text2CAD | 29.29 | 0.37 | 3.75 |
+| **CAD-Coder (Ours)** | **6.54** | **0.17** | **1.45** |
+
+*CD metrics are ×10³. Lower is better.*
+
+## Training Details
+
+- **Base Model**: Qwen2.5-7B-Instruct
+- **Training Data**: 110K text-CadQuery-3D model triplets + 1.5K CoT samples
+- **Hardware**: 8× NVIDIA A800 80GB GPUs
+- **Framework**: Hugging Face Transformers, DeepSpeed, Verl (GRPO)
+
+## Citation
+
+```bibtex
+@article{guan2025cadcoder,
+  title={CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward},
+  author={Guan, Yandong and Wang, Xilin and Xing, Ximing and Zhang, Jing and Xu, Dong and Yu, Qian},
+  journal={arXiv preprint arXiv:2505.19713},
+  year={2025}
+}
+```
+
+## License
+
+This model is released under the Apache 2.0 License, following the base model (Qwen2.5-7B-Instruct) license terms.
+
+## Acknowledgements
+
+- Base model: [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+- Training data derived from [Text2CAD](https://github.com/sadilkhan/Text2CAD) dataset